Responsible AI for Developers: Privacy and Safety Essentials

As AI systems become integrated into everyday software, developers face the critical challenge of ensuring these models are safe, secure, and respectful of user privacy. Building AI responsibly is no longer optional—it is a core software engineering requirement. This text-based course guides you through the foundational principles and practical strategies needed to implement robust AI safety and privacy protections. You will transition from understanding basic ethical concepts to actively applying modern defense-in-depth techniques to secure your AI pipelines and model interactions. What you'll learn: • Understand the core pillars of responsible AI, including fairness, transparency, and accountability. • Implement privacy-preserving techniques such as data anonymization, differential privacy, and secure data handling. • Protect AI applications against modern vulnerabilities, including prompt injection, data poisoning, and model extraction attacks. • Configure safety filters and content moderation layers to prevent harmful or biased model outputs. • Apply secure retrieval-augmented generation patterns to protect sensitive data in vector databases. • Evaluate AI models for bias and compliance with emerging global AI safety standards. The course starts with key terminology, basic concepts, and foundational definitions before moving into practical written guides on securing data pipelines and monitoring live applications. This course is designed specifically for beginners, software developers, and aspiring AI practitioners with no prior safety prerequisites. Start reading today to build AI applications that users can trust.